U.S. patent application number 14/709574 was filed with the patent office on 2016-11-17 for automatic adjustment of helmet parameters based on a category of play.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to JAMES R. KOZLOSKI, MARK C. H. LAMOREY, CLIFFORD A. PICKOVER, JOHN J. RICE.
Application Number | 20160331318 14/709574 |
Document ID | / |
Family ID | 57275779 |
Filed Date | 2016-11-17 |
United States Patent
Application |
20160331318 |
Kind Code |
A1 |
KOZLOSKI; JAMES R. ; et
al. |
November 17, 2016 |
AUTOMATIC ADJUSTMENT OF HELMET PARAMETERS BASED ON A CATEGORY OF
PLAY
Abstract
Embodiments include method, systems and computer program
products for automatic adjustment of helmet parameters based on a
category of play. Aspects include monitoring a plurality of sensors
in a helmet and determining the category of play for a user of the
helmet based on data received from the plurality of sensors.
Aspects further include automatically adjusting one or more
parameters of the helmet based on the category of play.
Inventors: |
KOZLOSKI; JAMES R.; (NEW
FAIRFIELD, CT) ; LAMOREY; MARK C. H.; (WILLISTON,
VT) ; PICKOVER; CLIFFORD A.; (YORKTOWN HEIGHTS,
NY) ; RICE; JOHN J.; (MOHEGAN LAKE, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
57275779 |
Appl. No.: |
14/709574 |
Filed: |
May 12, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A42B 3/046 20130101;
A42B 3/0433 20130101; A61B 5/6803 20130101; A61F 5/3707 20130101;
A61B 2503/10 20130101; A61B 2503/22 20130101; A61B 5/7275 20130101;
A61B 5/4064 20130101; A42B 3/324 20130101; A61B 5/1123
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A42B 3/08 20060101 A42B003/08; A61F 5/37 20060101
A61F005/37; A42B 3/12 20060101 A42B003/12 |
Claims
1. (canceled)
2. (canceled)
3. (canceled)
4. (canceled)
5. (canceled)
6. (canceled)
7. (canceled)
8. A computer program product for automatic adjustment of helmet
parameters based on a category of play, the computer program
product comprising: a non-transitory storage medium readable by a
processing circuit and storing instructions for execution by the
processing circuit for performing a method comprising: monitoring a
plurality of sensors in a helmet; determining the category of play
for a user of the helmet based on data received from the plurality
of sensors; and automatically adjusting one or more parameters of
the helmet based on the category of play.
9. The computer program product of claim 8, wherein automatically
adjusting one or more parameters of the helmet based on the
category of play includes adjusting at least one of a stiffness, a
size and a shape of a padding of the helmet.
10. The computer program product of claim 9, wherein the padding is
disposed on an internal surface of the helmet.
11. The computer program product of claim 9, the stiffness of the
padding on the helmet includes increasing the stiffness in a first
area of the padding and decreasing the stiffness in a second area
of the padding.
12. The computer program product of claim 8, wherein automatically
adjusting one or more parameters of the helmet based on the
category of play includes adjusting a tightness of a chin strap of
the helmet.
13. The computer program product of claim 12, wherein the one or
more parameters are adjusted based on a risk profile associated
with the category of play.
14. The computer program product of claim 13, wherein the risk
profile is further based on a medical history of the user of the
helmet.
15. An adjustable helmet for mitigating the risk of brain injuries,
comprising: a processor and one or more sensors, the processor
configured to: monitoring the one or more sensors; determine the
category of play for a user of the helmet based on data received
from the plurality of sensors; and automatically adjust one or more
parameters of the helmet based on the category of play.
16. The helmet of claim 15, wherein automatically adjusting one or
more parameters of the helmet based on the category of play
includes adjusting at least one of a stiffness, a size and a shape
of a padding of the helmet.
17. The helmet of claim 16, wherein the padding is disposed on an
internal surface of the helmet.
18. The helmet of claim 16, the stiffness of the padding on the
helmet includes increasing the stiffness in a first area of the
padding and decreasing the stiffness in a second area of the
padding.
19. The helmet of claim 15, wherein the one or more parameters are
adjusted based on a risk profile associated with the category of
play.
20. The helmet of claim 19, wherein the risk profile is further
based on a medical history of the user of the helmet.
Description
BACKGROUND
[0001] The present disclosure relates to the adjustment of helmet
parameters to mitigate the risk of brain injuries, and more
specifically, to methods, systems and computer program products for
the automatic adjustment of helmet parameters based on a category
of play to mitigate the risk of brain injuries.
[0002] Generally speaking, safety is a primary concern for both
users of helmets and manufacturers of helmets. Helmets are used by
individuals that participate in activities that have risk of head
trauma, such as the area of sports, biking, motorcycling, etc.
While helmets have traditionally been used to provide protection
from blunt force trauma to the head, an increased awareness of
concussion causing forces has motivated a need for advances in
helmet technology to provide increased protection against
concussions. A concussion is a type of traumatic brain injury that
is caused by a blow to the head that shakes the brain inside the
skull due to linear or rotational accelerations. Recently, research
has linked concussions to a range of health problems, from
depression to Alzheimer's, along with a range of brain injuries.
Unlike severe traumatic brain injuries, which result in lesions or
bleeding inside the brain and are detectable using standard medical
imaging, a concussion is often invisible in brain tissue, and
therefore only detectable by means of a cognitive change, where
that change is measurable by changes to brain tissue actions,
either neurophysiological or through muscle actions caused by the
brain and the muscles resulting effects on the environment, for
example, speech sounds.
[0003] Currently available helmets include a hard outer shell and
internal padding that is designed to mitigate the risk of brain
injuries. These helmets are designed to accommodate all types of
impacts regardless of the probability of the occurrence of specific
impacts during various types of usage.
SUMMARY
[0004] In accordance with an embodiment, a method for the automatic
adjustment of helmet parameters based on a category of play
includes monitoring a plurality of sensors in a helmet and
determining the category of play for a user of the helmet based on
data received from the plurality of sensors. Aspects further
include automatically adjusting one or more parameters of the
helmet based on the category of play.
[0005] In accordance with another embodiment, an adjustable helmet
for mitigating the risk of brain injuries includes a processor and
one or more sensors, the processor configured for performing a
method. The method includes monitoring a plurality of sensors in a
helmet and determining the category of play for a user of the
helmet based on data received from the one or more of sensors.
Aspects further include automatically adjusting one or more
parameters of the helmet based on the category of play.
[0006] In accordance with a further embodiment, a computer program
product for the automatic adjustment of helmet parameters based on
a category of play includes a non-transitory storage medium
readable by a processing circuit and storing instructions for
execution by the processing circuit for performing a method. The
method includes monitoring a plurality of sensors in a helmet and
determining the category of play for a user of the helmet based on
data received from the plurality of sensors. Aspects further
include automatically adjusting one or more parameters of the
helmet based on the category of play.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The subject matter which is regarded as the invention is
particularly pointed out and distinctly claimed in the claims at
the conclusion of the specification. The forgoing and other
features, and advantages of the invention are apparent from the
following detailed description taken in conjunction with the
accompanying drawings in which:
[0008] FIG. 1 is a block diagram illustrating one example of a
processing system for practice of the teachings herein;
[0009] FIG. 2 is a block diagram illustrating an adjustable helmet
in accordance with an exemplary embodiment;
[0010] FIG. 3 is a flow diagram of a method for the automatic
adjustment of helmet parameters based on a category of play in
accordance with an exemplary embodiment;
[0011] FIG. 4 is a flow diagram of another method for automatic
adjustment of helmet parameters based on a category of play in
accordance with an exemplary embodiment; and
[0012] FIG. 5 is a block diagram illustrating a system for
monitoring adjustable helmets in accordance with an exemplary
embodiment.
DETAILED DESCRIPTION
[0013] In accordance with exemplary embodiments of the disclosure,
methods, systems and computer program products for the automatic
adjustment of helmet parameters based on a category of play are
provided. In exemplary embodiments, the helmets include one or more
sensors and one or more adjustable parameters, such as an
adjustable chin strap or adjustable internal or external padding.
In exemplary embodiments, the sensors may include one or more
accelerometers, gyroscopes, or the like. In one embodiment, the
outputs of the sensors are provided to a processor that monitors
one or more physical movements or actions of the user and
determines a category of play of the user of the helmet. In other
embodiments, the category of play of the user may be provided to
the helmet by the user or by another source. In exemplary
embodiments, the processor makes adjustments to a protection
profile of the helmet based on the category of play. The protection
profile of the helmet may include, but is not limited to, the
tightness of the chin strap, the size of one or more pads of the
helmet, the stiffness of one or more pads of the helmet and the
lateral mobility of one or more pads of the helmet. In exemplary
embodiments, the protection profile for each category of play can
be determined based on a probability of certain types of impacts
and events occurring during each category of play.
[0014] Referring to FIG. 1, there is shown an embodiment of a
processing system 100 for implementing the teachings herein. In
this embodiment, the system 100 has one or more central processing
units (processors) 101a, 101b, 101c, etc. (collectively or
generically referred to as processor(s) 101). In one embodiment,
each processor 101 may include a reduced instruction set computer
(RISC) microprocessor. Processors 101 are coupled to system memory
114 and various other components via a system bus 113. Read only
memory (ROM) 102 is coupled to the system bus 113 and may include a
basic input/output system (BIOS), which controls certain basic
functions of system 100.
[0015] FIG. 1 further depicts an input/output (I/O) adapter 107 and
a network adapter 106 coupled to the system bus 113. I/O adapter
107 may be a small computer system interface (SCSI) adapter that
communicates with a hard disk 103 and/or tape storage drive 105 or
any other similar component. I/O adapter 107, hard disk 103, and
tape storage device 105 are collectively referred to herein as mass
storage 104. Operating system 120 for execution on the processing
system 100 may be stored in mass storage 104. A network adapter 106
interconnects bus 113 with an outside network 116 enabling data
processing system 100 to communicate with other such systems. A
screen (e.g., a display monitor) 115 is connected to system bus 113
by display adaptor 112, which may include a graphics adapter to
improve the performance of graphics intensive applications and a
video controller. In one embodiment, adapters 107, 106, and 112 may
be connected to one or more I/O busses that are connected to system
bus 113 via an intermediate bus bridge (not shown). Suitable I/O
buses for connecting peripheral devices such as hard disk
controllers, network adapters, and graphics adapters typically
include common protocols, such as the Peripheral Component
Interconnect (PCI). Additional input/output devices are shown as
connected to system bus 113 via user interface adapter 108 and
display adapter 112. A keyboard 109, mouse 110, and speaker 111 all
interconnected to bus 113 via user interface adapter 108, which may
include, for example, a Super I/O chip integrating multiple device
adapters into a single integrated circuit.
[0016] Thus, as configured in FIG. 1, the system 100 includes
processing capability in the form of processors 101, storage
capability including system memory 114 and mass storage 104, input
means such as keyboard 109 and mouse 110, and output capability
including speaker 111 and display 115. In one embodiment, a portion
of system memory 114 and mass storage 104 collectively store an
operating system such as the AIX.RTM. operating system from IBM
Corporation to coordinate the functions of the various components
shown in FIG. 1.
[0017] Referring to FIG. 2, a block diagram illustrating an
adjustable helmet 200 in accordance with an exemplary embodiment is
shown. The term "helmet" may include, but is not intended to be
limited to, a football helmet, a motorcycle helmet or the like. In
exemplary embodiments, the adjustable helmet 200 includes one or
more of the following: an accelerometer 202, a chin strap 204, a
padding 206, a gyroscope 208, a processor 210, a transceiver 212, a
power supply 214 and a memory 216. In exemplary embodiments, the
power supply 214 may be a battery configured to provide power to
one or more of the accelerometer 202, the gyroscope 208, the
processor 210 and the transceiver 212.
[0018] In one embodiment, the processor 210 is configured to
receive an output from one or more of the accelerometer 202 and the
gyroscope 208 and to determine a category of play of the user of
the adjustable helmet 200. In other embodiments, the processor 210
may be provided with the category of play of the user of the
adjustable helmet 200 or the helmet may receive the category of
play from an external processing system via the transceiver 212. As
used herein, the term category of play means the manner in which
the user is using the helmet. In one example, for a football
helmet, the category of play may refer to the position being played
by the user, running back, wide receiver, linemen, etc., or by the
current activity being performed by the user, running, blocking,
jumping, etc.
[0019] In exemplary embodiments, the padding 206 of the adjustable
helmet 200 may include either or both of internal padding or
external padding that can have one or more adjustable parameters.
In one embodiment, the padding 206 may include electroactive
polymers that can be used to change the size, shape, and/or
stiffness of the padding 206. In another embodiment, the padding
206 may include inflatable padding that can be inflated and
deflated by the adjustable helmet 200. In additional embodiments,
the padding 206 may be coupled to the helmet 200 by a liner that
can be adjusted to selectively allow the padding 206 to move
laterally in relation to the shell of the helmet. For example, the
liner may be configured to allow the padding to slide, or slip,
along the surface of the shell of the helmet to reduce the torque
on the user's head during an impact. In exemplary embodiments, the
degree, or amount of lateral movement, of the liner with respect to
the shell may be automatically adjusted based on the category of
play of the user of the helmet.
[0020] Referring now to FIG. 3, a flow diagram of a method 300 for
the automatic adjustment of helmet parameters based on a category
of play in accordance with an exemplary embodiment is shown. As
shown at block 302, the method 300 includes monitoring a plurality
of sensors in an adjustable helmet. In exemplary embodiments, the
plurality of sensors includes one or more of an accelerometer and a
gyroscope. Next, as shown at block 304, the method 300 includes
determining a category of play for a user of the helmet based on
data received from the plurality of sensors. In exemplary
embodiments, the processor of the adjustable helmet may create a
baseline profile of the user during each category of play based on
input form the accelerometer and the gyroscope and may store the
baseline profile in the memory. The processor may compare the
readings from the accelerometer and the gyroscope with the stored
baseline profile to determine the category of play for a user.
[0021] Continuing with reference to FIG. 3, as shown at block 306,
the method 300 includes automatically adjusting one or more
parameters of the helmet based on the category of play. In
exemplary embodiments, the adjustments may include, but are not
limited to, adjusting the stiffness on the padding, adjusting the
tightness of the chin strap, adjusting the size of the padding,
etc. The type and amount of an adjustment is chosen based on a
model of expected risks given certain plays to certain types of
hits and accelerations to mitigate the brain injury from these hits
and accelerations. The adjustments to the padding may be uniform or
non-uniform, i.e., the stiffness of all of the padding may not be
the same. For example, based on the category of play the stiffness
of the padding in the front portion of the helmet may be greater or
less that the stiffness in the back of the helmet.
[0022] Optionally, the method 300 may include storing the
determined states of play and adjustments made to the one or more
parameters in a memory, as shown in block 308. In addition, the
method 300 may also include transmitting the data received from the
plurality of sensors, the determined categories of play and
adjustments made to the one or more parameters to a processing
system, as shown at block 310.
[0023] Referring now to FIG. 4, a flow diagram of another method
400 for the automatic adjustment of helmet parameters based on a
category of play in accordance with an exemplary embodiment is
shown. As shown at block 402, the method 400 includes receiving a
category of play for a user of a helmet. For example, the helmet
may receive an input from the user that indicates the category of
play or the helmet may receive input from a separate processing
system via a transmitter that indicates the category of play. Next,
as shown at decision block 404, the method 400 includes
automatically adjusting one or more parameters of the helmet based
on the category of play. In exemplary embodiments, the adjustments
may include, but are not limited to, adjusting the stiffness on the
padding, adjusting the tightness of the chin strap, adjusting the
size of the padding, etc. The type and amount of an adjustment is
chosen based on a model of expected risks given certain plays to
certain types of hits and accelerations to mitigate the brain
injury from these hits and accelerations. The adjustments to the
padding may be uniform or non-uniform, i.e., the stiffness of all
of the padding may not be the same. For example, based on the
category of play the stiffness of the padding in the front portion
of the helmet may be greater or less that the stiffness in the back
of the helmet. Optionally, the method 400 may include transmitting
data received from plurality of sensors disposed in the helmet, the
category of play and adjustments made to the one or more parameters
to a processing system, as shown in block 406.
[0024] In exemplary embodiments, the processor of the helmet may
use a risk/probability function that is constructed based on the
category of play a wearer is engaged in to determine the parameters
for the adjustable helmet for certain hits and certain angles of
acceleration. In one embodiment, such as that shown in FIG. 4, the
risk/probability function may receive a direct input of category of
play, e.g. from a quarterback calling a certain play, or from a
motorcycle signaling its speed, turn frequency, aggressiveness of
the rider, ice on road, etc. In one embodiment, such as that shown
in FIG. 3, the risk/probability function may include making a
determination of the category of play based on data received from
one or more sensors disposed in the helmet.
[0025] Referring now to FIG. 5, a block diagram illustrating a
system 500 for monitoring adjustable helmets in accordance with an
exemplary embodiment is shown. As illustrated the system 500
includes one or more adjustable helmets 502, such as the one shown
and described above with reference to FIG. 2, and a processing
system 504, such as the one shown and described above with
reference to FIG. 1. The processing system 504 is configured to
communicate with the helmets 502 and is also configured to store
the medical history 506 of the users of the helmets 502. In
exemplary embodiments, the medical history 506 of the users of the
helmets 502 may be used by the helmet in determining what
adjustments to make to the helmet during play. In addition, the
processing system 504 may include a virtual world display 508 that
is configured to provide a display a real-time status of each of
the users of the helmets. In exemplary embodiments, the status may
include the category of play of each user, any indications that the
user may have suffered a traumatic brain injury, a duration of play
of the user, a duration that the user has been in the current
category of play, or the like.
[0026] In exemplary embodiments, the user's history of collision or
medical concerns may be used to determine a traumatic brain injury
risk assessment, either by the embedded processor or the separate
processing system. In addition, the helmet may be configured to
provide a real-time feed of the user's cognitive state to increase
the confidence level of the need for a particular alert or
indication. In exemplary embodiments, an aggregate indication may
be used to summarize an overall state of a group of players. This
may also help to potentially identify area of risk in the dynamics
of player-player interaction, overly aggressive players, playing
field conditions, etc. In exemplary embodiments, an automatic feed
from a user's history of collision or medical concerns may also be
provided to a processor of the helmet in order to update an impact
risk model for each category of play. In addition, the processing
system 504 may receive a real-time feed of the user's cognitive
state, which can be used to update the risk models used by the
helmets. The risk models may also be sent to the virtual world
display 508 of the game and players, which allows the sports staff
health professionals to visualize the nature of potential
problems.
[0027] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0028] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0029] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0030] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0031] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0032] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0033] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0034] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *